Iterative decoding architecture with harq combining and soft decision directed channel estimation

ABSTRACT

Certain aspects of the present disclosure relate to a method for iterative decoding with re-transmissions of data and to a method for iterative decoding with soft decision directed channel estimation.

CLAIM OF PRIORITY UNDER 35 U.S.C. §120

The present application is a continuation-in-part of co-pending U.S.patent application Ser. No. 12/552,673 entitled, “Unified IterativeDecoding Architecture using Joint LLR Extraction and A PrioriProbability,” filed Sep. 2, 2009, and assigned to the assignee hereofand hereby expressly incorporated by reference herein.

BACKGROUND

1. Field

Certain aspects of the present disclosure generally relate to wirelesscommunications and, more particularly, to iterative decoding withre-transmissions and to iterative decoding with soft decision directedchannel estimation.

2. Background

Iterative demodulation-decoding structure can be employed at a wirelessreceiver side to enhance error rate performance. Channel estimates thatare utilized for receiver processing can be typically obtained based ona known pilot signal, while actual data are not used. Therefore, furtherimprovement of the error rate performance can be achieved by refiningthe channel estimates during the iterative demodulation-decodingalgorithm by using available reliability information associated withtransmitted data.

On the other hand, the Hybrid Automatic Repeat Request (HARQ) approachcan be applied in a wireless communications system to improve itsquality of service (QoS). The QoS improvement can be achieved byre-transmitting data if a current level of QoS is below a definedthreshold value. It is proposed in the present disclosure to efficientlycombine the HARQ approach with the iterative demodulation-decodingreceiver structure.

SUMMARY

Certain aspects provide a method for wireless communications. The methodgenerally includes receiving at least one data stream, de-mapping anddecoding the received data stream in an iterative manner to compute aset of a posteriori log-likelihood ratios (LLRs) of bits of the receiveddata stream, storing the computed set of a posteriori LLRs, if thedecoding is performed in the iterative manner a defined number of times,receiving re-transmitted the data stream, and de-mapping and decodingthe received re-transmitted data stream in the iterative manner usingthe stored set of LLRs.

Certain aspects provide an apparatus for wireless communications. Theapparatus generally includes a receiver configured to receive at leastone data stream, a de-mapper and a decoder configured to de-map anddecode the received data stream in an iterative manner to compute aposteriori log-likelihood ratios (LLRs) of bits of the received datastream, and a buffer configured to store the computed a posteriori LLRs,if the decoding is performed in the iterative manner a defined number oftimes, wherein the receiver is also configured to receive re-transmitteddata stream, and wherein the de-mapper and the decoder are alsoconfigured to de-map and decode the received re-transmitted data streamin the iterative manner using the stored set of LLRs.

Certain aspects provide an apparatus for wireless communications. Theapparatus generally includes means for receiving at least one datastream, means for de-mapping and decoding the received data stream in aniterative manner to compute a posteriori log-likelihood ratios (LLRs) ofbits of the received data stream, means for storing the computed aposteriori LLRs, if the decoding is performed in the iterative manner adefined number of times, means for receiving re-transmitted the datastream, and means for de-mapping and decoding the receivedre-transmitted data stream in the iterative manner using the stored setof LLRs.

Certain aspects provide a computer-program product for wirelesscommunications. The computer-program product includes acomputer-readable medium comprising instructions executable to receiveat least one data stream, de-map and decode the received data stream inan iterative manner to compute a posteriori log-likelihood ratios (LLRs)of bits of the received data stream, store the computed a posterioriLLRs, if the decoding is performed in the iterative manner a definednumber of times, receive re-transmitted data stream, and de-map anddecode the received re-transmitted data stream in the iterative mannerusing the stored set of LLRs.

Certain aspects provide a wireless node. The wireless node generallyincludes at least one antenna, a receiver configured to receive at leastone data stream via the at least one antenna, a de-mapper and a decoderconfigured to de-map and decode the received data stream in an iterativemanner to compute a posteriori log-likelihood ratios (LLRs) of bits ofthe received data stream, and a buffer configured to store the computeda posteriori LLRs, if the decoding is performed in the iterative mannera defined number of times, wherein the receiver is also configured toreceive re-transmitted data stream via the at least one antenna, andwherein the de-mapper and the decoder are also configured to de-map anddecode the received re-transmitted data stream in the iterative mannerusing the stored set of LLRs.

Certain aspects provide a method for wireless communications. The methodgenerally includes receiving a pilot signal and at least one data streamtransmitted over a wireless channel, computing initial estimates of thewireless channel using the received pilot signal, de-mapping, de-ratematching and decoding the received data stream using the computedinitial estimates of the wireless channel to compute a set oflog-likelihood ratios (LLRs) of transmitted bits of the at least onedata stream, updating estimates of the wireless channel using thecomputed set of LLRs and the computed initial estimates of the wirelesschannel, and de-mapping, de-rate matching and decoding the received datastream using the updated estimates of the wireless channel.

Certain aspects provide an apparatus for wireless communications. Theapparatus generally includes a receiver configured to receive a pilotsignal and at least one data stream transmitted over a wireless channel,an estimator configured to compute initial estimates of the wirelesschannel using the received pilot signal, and a de-mapper, a de-ratematching circuit and a decoder configured to de-map, de-rate match anddecode the received data stream using the computed initial estimates ofthe wireless channel to compute a set of log-likelihood ratios (LLRs) oftransmitted bits of the data stream, wherein the estimator is alsoconfigured to update estimates of the wireless channel using thecomputed set of LLRs and the computed initial estimates of the wirelesschannel, and wherein the de-mapper, the de-rate matching circuit and thedecoder are also configured to de-map, de-rate match and decode thereceived data stream using the updated estimates of the wirelesschannel.

Certain aspects provide an apparatus for wireless communications. Theapparatus generally includes means for receiving a pilot signal and atleast one data stream transmitted over a wireless channel, means forcomputing initial estimates of the wireless channel using the receivedpilot signal, means for de-mapping, de-rate matching and decoding thereceived data stream using the computed initial estimates of thewireless channel to compute a set of log-likelihood ratios (LLRs) oftransmitted bits of the data stream, means for updating estimates of thewireless channel using the computed set of LLRs and the computed initialestimates of the wireless channel, and means for de-mapping, de-ratematching and decoding the received data stream using the updatedestimates of the wireless channel.

Certain aspects provide a computer-program product for wirelesscommunications. The computer-program product includes acomputer-readable medium comprising instructions executable to receive apilot signal and at least one data stream transmitted over a wirelesschannel, compute initial estimates of the wireless channel using thereceived pilot signal, de-map, de-rate match and decode the receiveddata stream using the computed initial estimates of the wireless channelto compute a set of log-likelihood ratios (LLRs) of transmitted bits ofthe data stream, update estimates of the wireless channel using thecomputed set of LLRs and the computed initial estimates of the wirelesschannel, and de-map, de-rate match and decode the received data streamusing the updated estimates of the wireless channel.

Certain aspects provide a wireless node. The wireless node generallyincludes at least one antenna, a receiver configured to receive via theat least one antenna a pilot signal and at least one data streamtransmitted over a wireless channel, an estimator configured to computeinitial estimates of the wireless channel using the received pilotsignal, and a de-mapper, a de-rate matching circuit and a decoderconfigured to de-map, de-rate match and decode the received data streamusing the computed initial estimates of the wireless channel to computea set of log-likelihood ratios (LLRs) of transmitted bits of the datastream, wherein the estimator is also configured to update estimates ofthe wireless channel using the computed set of LLRs and the computedinitial estimates of the wireless channel, and wherein the de-mapper,the de-rate matching circuit and the decoder are also configured tode-map, de-rate match and decode the received data stream using theupdated estimates of the wireless channel.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above-recited features of the presentdisclosure can be understood in detail, a more particular description,briefly summarized above, may be had by reference to aspects, some ofwhich are illustrated in the appended drawings. It is to be noted,however, that the appended drawings illustrate only certain typicalaspects of this disclosure and are therefore not to be consideredlimiting of its scope, for the description may admit to other equallyeffective aspects.

FIG. 1 illustrates an example wireless communication system, inaccordance with certain aspects of the present disclosure.

FIG. 2 illustrates various components that may be utilized in a wirelessdevice in accordance with certain aspects of the present disclosure.

FIG. 3 illustrates an example transmitter that may be used within awireless communication system in accordance with certain aspects of thepresent disclosure.

FIG. 4 illustrates an example receiver with iterative decoding structurethat may be used within a wireless communication system in accordancewith certain aspects of the present disclosure.

FIG. 5 illustrates an example block diagram of demodulator as a part ofthe iterative receiver from FIG. 4 in accordance with certain aspects ofthe present disclosure.

FIG. 6 illustrates an example iterative receiver with Hybrid AutomaticRepeat Request (HARQ) combining in accordance with certain aspects ofthe present disclosure.

FIG. 7 illustrates example operations for iterative decoding with HARQcombining in accordance with certain aspects of the present disclosure.

FIG. 7A illustrates example components capable of performing theoperations illustrated in FIG. 7.

FIG. 8 illustrates an example iterative receiver with HARQ combiningwith reduced memory requirements in accordance with certain aspects ofthe present disclosure.

FIG. 9 illustrates an example iterative receiver with soft decisiondirected channel estimation in accordance with certain aspects of thepresent disclosure.

FIG. 10 illustrates example operations for iterative decoding with softdecision directed channel estimation in accordance with certain aspectsof the present disclosure.

FIG. 10A illustrates example components capable of performing theoperations illustrated in FIG. 10.

DETAILED DESCRIPTION

Various aspects of the disclosure are described more fully hereinafterwith reference to the accompanying drawings. This disclosure may,however, be embodied in many different forms and should not be construedas limited to any specific structure or function presented throughoutthis disclosure. Rather, these aspects are provided so that thisdisclosure will be thorough and complete, and will fully convey thescope of the disclosure to those skilled in the art. Based on theteachings herein one skilled in the art should appreciate that the scopeof the disclosure is intended to cover any aspect of the disclosuredisclosed herein, whether implemented independently of or combined withany other aspect of the disclosure. For example, an apparatus may beimplemented or a method may be practiced using any number of the aspectsset forth herein. In addition, the scope of the disclosure is intendedto cover such an apparatus or method which is practiced using otherstructure, functionality, or structure and functionality in addition toor other than the various aspects of the disclosure set forth herein. Itshould be understood that any aspect of the disclosure disclosed hereinmay be embodied by one or more elements of a claim.

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration.” Any aspect described herein as “exemplary”is not necessarily to be construed as preferred or advantageous overother aspects.

Although particular aspects are described herein, many variations andpermutations of these aspects fall within the scope of the disclosure.Although some benefits and advantages of the preferred aspects arementioned, the scope of the disclosure is not intended to be limited toparticular benefits, uses, or objectives. Rather, aspects of thedisclosure are intended to be broadly applicable to different wirelesstechnologies, system configurations, networks, and transmissionprotocols, some of which are illustrated by way of example in thefigures and in the following description of the preferred aspects. Thedetailed description and drawings are merely illustrative of thedisclosure rather than limiting, the scope of the disclosure beingdefined by the appended claims and equivalents thereof.

An Example Wireless Communication System

The techniques described herein may be used for various broadbandwireless communication systems, including communication systems that arebased on an orthogonal multiplexing scheme and a single carriertransmission. Examples of such communication systems include OrthogonalFrequency Division Multiple Access (OFDMA) systems, Single-CarrierFrequency Division Multiple Access (SC-FDMA) systems, Code DivisionMultiple Access (CDMA), and so forth. An OFDMA system utilizesorthogonal frequency division multiplexing (OFDM), which is a modulationtechnique that partitions the overall system bandwidth into multipleorthogonal sub-carriers. These sub-carriers may also be called tones,bins, etc. With OFDM, each sub-carrier may be independently modulatedwith data. An SC-FDMA system may utilize interleaved FDMA (IFDMA) totransmit on sub-carriers that are distributed across the systembandwidth, localized FDMA (LFDMA) to transmit on a block of adjacentsub-carriers, or enhanced FDMA (EFDMA) to transmit on multiple blocks ofadjacent sub-carriers. In general, modulation symbols are sent in thefrequency domain with OFDM and in the time domain with SC-FDMA. A CDMAsystem may utilize spread-spectrum technology and a coding scheme whereeach transmitter (i.e., user) is assigned a code in order to allowmultiple users to be multiplexed over the same physical channel. TheCDMA system may utilize, for example, Wideband Code Division MultipleAccess (W-CDMA) protocol, High Speed Packet Access (HSPA) protocol,evolved Speed Packet Access (HSPA+) protocol, etc.

The teachings herein may be incorporated into (e.g., implemented withinor performed by) a variety of wired or wireless apparatuses (e.g.,nodes). In some aspects, a node implemented in accordance with theteachings herein may comprise an access point or an access terminal.

An access point (“AP”) may comprise, be implemented as, or known asNodeB, Radio Network Controller (“RNC”), eNodeB, Base Station Controller(“BSC”), Base Transceiver Station (“BTS”), Base Station (“BS”),Transceiver Function (“TF”), Radio Router, Radio Transceiver, BasicService Set (“BSS”), Extended Service Set (“ESS”), Radio Base Station(“RBS”), or some other terminology.

An access terminal (“AT”) may comprise, be implemented as, or known asan access terminal, a subscriber station, a subscriber unit, a mobilestation, a remote station, a remote terminal, a user terminal, a useragent, a user device, user equipment, or some other terminology. In someimplementations an access terminal may comprise a cellular telephone, acordless telephone, a Session Initiation Protocol (“SIP”) phone, awireless local loop (“WLL”) station, a personal digital assistant(“PDA”), a handheld device having wireless connection capability, orsome other suitable processing device connected to a wireless modem.Accordingly, one or more aspects taught herein may be incorporated intoa phone (e.g., a cellular phone or smart phone), a computer (e.g., alaptop), a portable communication device, a portable computing device(e.g., a personal data assistant), an entertainment device (e.g., amusic or video device, or a satellite radio), a global positioningsystem device, or any other suitable device that is configured tocommunicate via a wireless or wired medium. In some aspects the node isa wireless node. Such wireless node may provide, for example,connectivity for or to a network (e.g., a wide area network such as theInternet or a cellular network) via a wired or wireless communicationlink.

FIG. 1 illustrates an example of a wireless communication system 100 inwhich embodiments of the present disclosure may be employed. Thewireless communication system 100 may be a broadband wirelesscommunication system. The wireless communication system 100 may providecommunication for a number of cells 102, each of which is serviced by abase station 104. A base station 104 may be a fixed station thatcommunicates with user terminals 106. The base station 104 mayalternatively be referred to as an access point, a Node B or some otherterminology.

FIG. 1 depicts various user terminals 106 dispersed throughout thesystem 100. The user terminals 106 may be fixed (i.e., stationary) ormobile. The user terminals 106 may alternatively be referred to asremote stations, access terminals, terminals, subscriber units, mobilestations, stations, user equipment, etc. The user terminals 106 may bewireless devices, such as cellular phones, personal digital assistants(PDAs), handheld devices, wireless modems, laptop computers, personalcomputers, etc.

A variety of algorithms and methods may be used for transmissions in thewireless communication system 100 between the base stations 104 and theuser terminals 106. For example, signals may be sent and receivedbetween the base stations 104 and the user terminals 106 in accordancewith CDMA technique. If this is the case, the wireless communicationsystem 100 may be referred to as a CDMA system.

A communication link that facilitates transmission from a base station104 to a user terminal 106 may be referred to as a downlink (DL) 108,and a communication link that facilitates transmission from a userterminal 106 to a base station 104 may be referred to as an uplink (UL)110. Alternatively, a downlink 108 may be referred to as a forward linkor a forward channel, and an uplink 110 may be referred to as a reverselink or a reverse channel.

A cell 102 may be divided into multiple sectors 112. A sector 112 is aphysical coverage area within a cell 102. Base stations 104 within awireless communication system 100 may utilize antennas that concentratethe flow of power within a particular sector 112 of the cell 102. Suchantennas may be referred to as directional antennas.

FIG. 2 illustrates various components that may be utilized in a wirelessdevice 202 that may be employed within the wireless communication system100. The wireless device 202 is an example of a device that may beconfigured to implement the various methods described herein. Thewireless device 202 may be a base station 104 or a user terminal 106.

The wireless device 202 may include a processor 204 which controlsoperation of the wireless device 202. The processor 204 may also bereferred to as a central processing unit (CPU). Memory 206, which mayinclude both read-only memory (ROM) and random access memory (RAM),provides instructions and data to the processor 204. A portion of thememory 206 may also include non-volatile random access memory (NVRAM).The processor 204 typically performs logical and arithmetic operationsbased on program instructions stored within the memory 206. Theinstructions in the memory 206 may be executable to implement themethods described herein.

The wireless device 202 may also include a housing 208 that may includea transmitter 210 and a receiver 212 to allow transmission and receptionof data between the wireless device 202 and a remote location. Thetransmitter 210 and receiver 212 may be combined into a transceiver 214.A single or a plurality of transmit antennas 216 may be attached to thehousing 208 and electrically coupled to the transceiver 214. Thewireless device 202 may also include (not shown) multiple transmitters,multiple receivers, and multiple transceivers.

The wireless device 202 may also include a signal detector 218 that maybe used in an effort to detect and quantify the level of signalsreceived by the transceiver 214. The signal detector 218 may detect suchsignals as total energy, energy per subcarrier per symbol, powerspectral density and other signals. The wireless device 202 may alsoinclude a digital signal processor (DSP) 220 for use in processingsignals.

The various components of the wireless device 202 may be coupledtogether by a bus system 222, which may include a power bus, a controlsignal bus, and a status signal bus in addition to a data bus.

FIG. 3 illustrates an example of a transmitter 300 that may be usedwithin a wireless communication system 100 that utilizes CDMA. Portionsof the transmitter 300 may be implemented in the transmitter 210 of awireless device 202. The transmitter 300 may be implemented in a basestation 104 for transmitting data 302 to a user terminal 106 on adownlink 108. The transmitter 300 may also be implemented in a userterminal 106 for transmitting data 302 to a base station 104 on anuplink 110.

Data 302 to be transmitted represent a plurality of signals dedicated todifferent user terminals 106. Each signal from the plurality of signalsmay be spread in a spreading unit 306 by corresponding spreading codefrom a set of orthogonal spreading codes 304. The plurality of spreadsignals dedicated to different user terminals 106 may be summed togenerate a cumulative signal 308. The cumulative signal 308 to betransmitted is shown being provided as input to a mapper 310. The mapper310 may map the data stream 308 onto constellation points. The mappingmay be done using some modulation constellation, such as binaryphase-shift keying (BPSK), quadrature phase-shift keying (QPSK), 8phase-shift keying (8PSK), quadrature amplitude modulation (QAM), etc.Thus, the mapper 310 may output a symbol stream 312, which may representan input into a preamble insertion unit 314.

The preamble insertion unit 314 may be configured for inserting apreamble sequence at the beginning of the input symbol stream 312, andmay generate a corresponding data stream 316. The preamble may be knownat the receiver and may be utilized for time and frequencysynchronization, channel estimation, equalization and channel decoding.The output 316 of the preamble insertion unit 314 may then beup-converted to a desired transmit frequency band by a radio frequency(RF) front end 318. At least one antenna 320 may then transmit aresulting signal 322 over a wireless channel.

Certain aspects of the present disclosure support an iterative decodingarchitecture with sophisticated channel estimation that may beimplemented within the receiver 212. The proposed iterative receiverstructure may be also efficiently used at the receiver 212 forprocessing data re-transmissions in the case of Hybrid Automatic RepeatRequest (HARQ) communication mode.

Iterative Receiver Structure

Iterative demodulation-decoding approach may be applied at a wirelessreceiver to enhance its error rate performance. The iterative receiveralgorithm may comprise two steps that can be performed repeatedly in aninterleaved fashion. In one step, a posteriori probability for everytransmitted bit may be extracted. For example, a posteriori loglikelihood ratio (LLR) information may be obtained after certain numberof iterations of outer Turbo decoder on one or more data streams. Inanother step, the LLR of every transmitted bit may be generated.

In the very first iteration, LLRs may be generated directly from thereceived signal. For subsequent iterations, the extrinsic part of the aposteriori LLRs along with the received signal may be used to generatenew LLRs for the next iteration.

FIG. 4 illustrates an example receiver structure 400 based on iterativedemodulation-decoding algorithm that may be used within a wirelesscommunication system 100. The iterative structure 400 may be an integralpart of the receiver 212 from FIG. 2. The proposed iterative structure400 is based on LLR extraction jointly performed by a demapper 406 and aTurbo decoder 420.

At least one data stream of symbols transmitted over a wireless channelmay be received and stored in a write controller buffer (WCB) 401. Theiterative receiver structure 400 may comprise the demapper (i.e.,demodulator) 406 and the Turbo decoder 420 that can be iterativelyinterfaced. A Turbo joint log-likelihood ratio (JLLR) demodulation unit404 may generate a posteriori LLRs 410 of transmitted bits based onreceived samples 402 and based on a priori LLRs 408. Extrinsic LLRs 412may be obtained by subtracting the a priori LLRs 408 from the aposteriori LLRs 410.

The extrinsic LLRs 412 may be processed by a de-rate matching (DRM) unit414 to generate a priori LLRs 416 of an appropriate rate as input to theTurbo decoder 420. A Turbo decoding (TD) unit 418 may provide hardvalues of decoded bits 428. In order to improve error rate performance,outer feedback may be employed between the Turbo decoder 420 and thedemapper 406. The TD unit 418 may generate a posteriori LLRs 422 thatrepresent soft values of transmitted bits of the at least one datastream, while extrinsic LLRs 424 may be obtained by subtracting the apriori LLRs 416 from the a posteriori LLRs 422.

The extrinsic LLRs 424 may be processed by a rate matching (RM) unit 426to obtain the a priori LLRs 408 of an appropriate rate. The a prioriLLRs 408 may be utilized by the demodulator 406 in the next iteration.LLRs associated with a systematic portion of the transmitted bits andLLRs associated with a parity (i.e., redundant) portion of thetransmitted bits may be extracted from the extrinsic LLRs 424. Theextracted LLRs related to the systematic bits may be utilized forupdating the a priori LLRs 408 for the next processing iteration betweenthe demapper 406 and the decoder 420.

It can be noted that the proposed iterative decoder architecture 400 isdifferent from the well-known hard/soft serial interference cancellation(SIC) architecture. The hard/soft SIC architecture may only be used formultiple-input multiple-output (MIMO) systems, while the proposediterative decoding architecture may be used for both MIMO andsingle-input single-output (SISO) systems.

Important feature of the proposed iterative decoder 400 is that theextrinsic output 424 from the Turbo decoder 420 may become the a prioriprobability (APP) input 408 for the demapper 406 for the next iteration.Similarly, the extrinsic output 412 from the demapper 406 may become theAPP input 416 for the Turbo decoder 420. For the very first iteration,APP input may not be available to the demapper 406, and the LLR output410 of the demapper 406 may be equal to the extrinsic output 412.

Computation of Extrinsic Log Likelihood Ratios

FIG. 5 illustrates an example block diagram of the demapper 406 fromFIG. 4 that may generate extrinsic LLRs in accordance with certainaspects of the present disclosure. A received signal 500 may be whitenedand stored in a write controller buffer (WCB) unit 502. The storedwhitened received signal 504 may represent an input into the JLLRdemodulator unit 404 from FIG. 4. Another input in the demapper 406 maybe a matrix 506 of MIMO channel coefficients, and yet another input maybe a priori LLRs 508 from the Turbo decoder 420 from FIG. 4. The JLLRdemodulator unit 404 from FIG. 4 may generate a posteriori LLRs 510 oftransmitted coded bits. Extrinsic LLRs 512 may be obtained bysubtracting the a priori LLRs 508 from the a posteriori LLRs 510.

The system model may be represented as:

y=H·x+n,  (1)

where x is a vector of transmitted symbols from one or more transmitantennas, y is the whitened signal 504, H is the matrix 506 of channelcoefficients, and n is a noise vector. It can be assumed a MIMO wirelesssystem, while a single stream wireless system and a single-inputsingle-output (SISO) wireless system may be considered as special casesof the MIMO wireless system.

The LLR output 510 for the bit b_(k) from the vector of transmittedmodulated symbols x may be written as:

$\begin{matrix}\begin{matrix}{{L\left( b_{k} \right)} = {{LLR}\left( {b_{k}y} \right)}} \\{= {\log \left( \frac{P\left( {b_{k} = {0y}} \right)}{P\left( {b_{k} = {1y}} \right)} \right)}} \\{= {\log\left( \frac{\sum\limits_{{x:b_{k}} = 0}{P\left( {xy} \right)}}{\sum\limits_{{x:b_{k}} = 1}{P\left( {xy} \right)}} \right)}} \\{= {\log\left( \frac{\sum\limits_{{x:b_{k}} = 0}{{p\left( {yx} \right)}{P(x)}}}{\sum\limits_{{x:b_{k}} = 1}{{p\left( {yx} \right)}{P(x)}}} \right)}} \\{= {\log\left( \frac{\sum\limits_{{x:b_{k}} = 0}{{p\left( {yx} \right)}{P\left( {b_{k} = 0} \right)}{\prod\limits_{{i = 1},{i \neq k}}^{N \cdot M}{P\left( {b_{i} = u_{i}} \right)}}}}{\sum\limits_{{x:b_{k}} = 1}{{p\left( {yx} \right)}{P\left( {b_{k} = 1} \right)}{\prod\limits_{{j = 1},{j \neq k}}^{N \cdot M}{P\left( {b_{j} = v_{j}} \right)}}}} \right)}} \\{= {\underset{\underset{Extrinsic}{}}{\log\left( \frac{\sum\limits_{{x:b_{k}} = 0}{{p\left( {yx} \right)}{\prod\limits_{{i = 1},{i \neq k}}^{N \cdot M}{P\left( {b_{i} = u_{i}} \right)}}}}{\sum\limits_{{x:b_{k}} = 1}{{p\left( {yx} \right)}{\prod\limits_{{j = 1},{j \neq k}}^{N \cdot M}{P\left( {b_{j} = v_{j}} \right)}}}} \right)} + \underset{\underset{APP}{}}{\log\left( \frac{P\left( {b_{k} = 0} \right)}{P\left( {b_{k} = 1} \right)} \right)}}} \\{{= {{L_{E}\left( b_{k} \right)} + {L_{A}\left( b_{k} \right)}}},}\end{matrix} & (2)\end{matrix}$

where L(b_(k)) represents the a posteriori LLR output 510 of thedemodulator, L_(A)(b_(k)) is the a priori probability (APP) LLR input508 to the demodulator, L_(E)(b_(k)) is the extrinsic LLR output 512 ofthe demodulator, N is a number of transmitted MIMO data streams, and Mis a number of bits per modulation symbol. Variables u_(i) and v_(j) maybe either 0 or 1, depending on the symbol vector x and the symbol-bitmapping relation. In addition, according to the definition of LLRs, thea priori probabilities may be written as:

$\begin{matrix}{{{P\left( {b_{i} = 1} \right)} = \frac{1}{1 + {\exp \left( {L_{A}\left( b_{i} \right)} \right)}}},{{P\left( {b_{i} = 0} \right)} = {\frac{\exp \left( {L_{A}\left( b_{i} \right)} \right)}{1 + {\exp \left( {L_{A}\left( b_{i} \right)} \right)}}.}}} & (3)\end{matrix}$

It can be observed from equation (2) that the a posteriori LLR output ofthe demodulator may be composed of two parts, the APP information andthe extrinsic information. After combining equation (3) with equation(2), the extrinsic LLR for the bit b_(k) may be written as:

$\begin{matrix}\begin{matrix}{{\text{:}\mspace{14mu} {L_{E}\left( b_{k} \right)}} = {\log \left( \frac{\sum\limits_{{x:b_{k}} = 0}{{p\left( {yx} \right)}{\exp \left( {\sum\limits_{{i = 1},{i \neq k},{b_{i} = 0}}^{N \cdot M}{L_{A}\left( b_{i} \right)}} \right)}}}{\sum\limits_{{x:b_{k}} = 1}{{p\left( {yx} \right)}{\exp \left( {\sum\limits_{{j = 1},{j \neq k},{b_{j} = 0}}^{N \cdot M}{L_{A}\left( b_{j} \right)}} \right)}}} \right)}} \\{{= {\log \left( \frac{\sum\limits_{{x:b_{k}} = 0}{\exp \left( {{- \frac{{{y - {Hx}}}^{2}}{\sigma_{n}^{2}}} + {\sum\limits_{{i = 1},{i \neq k},{b_{i} = 0}}^{N \cdot M}{L_{A}\left( b_{i} \right)}}} \right)}}{\sum\limits_{{x:b_{k}} = 1}{\exp \left( {{- \frac{{{y - {Hx}}}^{2}}{\sigma_{n}^{2}}} + {\sum\limits_{{j = 1},{j \neq k},{b_{j} = 0}}^{N \cdot M}{L_{A}\left( b_{j} \right)}}} \right)}} \right)}},}\end{matrix} & (4)\end{matrix}$

where σ_(n) ² is a noise variance.

It can be observed from equation (4) that each term that represents adistance between a received symbol y and a transmitted hypothesis x maybe shifted by adding APP LLRs. In addition, conversion of the APP LLRsinto probabilities may not be required when utilizing the APPinformation.

The Max-Log MAP (MLM) solution may be obtained by replacing the sumoperation in equation (4) by a maximum operation, as given by:

$\begin{matrix}\begin{matrix}{{L_{E}\left( b_{k} \right)} = {\log \left( \frac{\sum\limits_{{x:b_{k}} = 0}{\exp \left( {{- \frac{{{y - {Hx}}}^{2}}{\sigma_{n}^{2}}} + {\sum\limits_{{i = 1},{i \neq k},{b_{i} = 0}}^{N \cdot M}{L_{A}\left( b_{i} \right)}}} \right)}}{\sum\limits_{{x:b_{k}} = 1}{\exp \left( {{- \frac{{{y - {Hx}}}^{2}}{\sigma_{n}^{2}}} + {\sum\limits_{{j = 1},{j \neq k},{b_{j} = 0}}^{N \cdot M}{L_{A}\left( b_{j} \right)}}} \right)}} \right)}} \\{\overset{MLM}{\approx}{\log \left( \frac{\max\limits_{{x:b_{k}} = 0}{\exp \left( {{- \frac{{{y - {Hx}}}^{2}}{\sigma_{n}^{2}}} + {\sum\limits_{{i = 1},{i \neq k},{b_{i} = 0}}^{N \cdot M}{L_{A}\left( b_{i} \right)}}} \right)}}{\max\limits_{{x:b_{k}} = 1}{\exp \left( {{- \frac{{{y - {Hx}}}^{2}}{\sigma_{n}^{2}}} + {\sum\limits_{{j = 1},{j \neq k},{b_{j} = 0}}^{N \cdot M}{L_{A}\left( b_{j} \right)}}} \right)}} \right)}} \\{= {{\max\limits_{{x:b_{k}} = 0}\left( {{- \frac{{{y - {Hx}}}^{2}}{\sigma_{n}^{2}}} + {\sum\limits_{{i = 1},{i \neq k},{b_{i} = 0}}^{N \cdot M}{L_{A}\left( b_{i} \right)}}} \right)} -}} \\{{\max\limits_{{x:b_{k}} = 1}{\left( {{- \frac{{{y - {Hx}}}^{2}}{\sigma_{n}^{2}}} + {\sum\limits_{{j = 1},{j \neq k},{b_{j} = 0}}^{N \cdot M}{L_{A}\left( b_{j} \right)}}} \right).}}}\end{matrix} & (5)\end{matrix}$

Iterative Decoding with HARQ Combining

FIG. 6 illustrates an example iterative receiver 600 with HybridAutomatic Repeat Request (HARQ) combining in accordance with certainaspects of the present disclosure. It can be observed that the receiver600 is based on the iterative receiver 400 from FIG. 4, where apre-Turbo decoding (pre-TD) buffer 632 and a post-TD buffer 636 may beincorporated to efficiently support re-transmission of data in the casewhen the decoding is not successful.

For the first transmission of data, a structure and processing path maybe identical as the structure 400 illustrated in FIG. 4. In particular,an extrinsic log-likelihood ratio (LLR) output 624 from a Turbo decoder620 may become an a priori probability (APP) input 608 for a demapper606 and for a Turbo JLLR unit 604 for the next processing iteration.Similarly, an extrinsic LLR output 612 from a demapper 606 may become anAPP input 616 for the Turbo decoder 620 and for a Turbo decoding unit618.

At the last outer iteration between the demapper 606 and the Turbodecoder 620 of the first data transmission, the extrinsic LLR output 634from the demapper 606 after being processed by a de-rate matching (DRM)unit 614 may be stored in the pre-TD buffer 632. The stored LLRs may beutilized for possible HARQ combining during re-transmission of data incase when a cyclic redundancy check (CRC) at the output 628 of the Turbodecoder 620 fails after a defined number of outer iterations between thedemapper 606 and the Turbo decoder 620. Also, the Turbo decoder outputLLRs 622 may be stored in the post-TD buffer 636. The stored LLRs 622may be then processed and used as APPs 608 for possible datare-transmission in the case when the CRC at the output 628 of the Turbodecoder 620 fails after the defined number of outer iterations of thefirst data transmission.

For the first iteration of the re-transmission, the LLRs 622 from theprevious transmission stored in the post-TD buffer 636 may be directlyutilized as APPs for the current re-transmission. On the other hand, theextrinsic LLR output 634 from the demapper 606 may be combined with theLLRs stored in the pre-TD buffer 632 (i.e., the LLRs saved during theprevious transmission) as a part of the HARQ combining process. Theoutput LLRs 616 may be then fed into the Turbo decoder 620.

For the next iteration of the re-transmission, the extrinsic LLRs 624from the Turbo decoder 620 may be first combined with the post-TD LLRscomputed and stored during the previous data transmission. The combinedLLRs 638 may be processed by a rate-matching (RM) unit 626 and thenutilized as APPs 608. The extrinsic output LLRs 634 from the demapper606 may be combined with the LLRs stored in the pre-TD buffer 632, andthen fed again into the Turbo decoder 620.

FIG. 7 summarizes example operations 700 for iterative decoding withHARQ combining in accordance with certain aspects of the presentdisclosure. At 710, at least one data stream may be received. At 720,de-mapping and decoding of the received at least one data stream may beperformed in an iterative manner. During the iterative process, a firstset of LLRs of transmitted bits of the at least one data stream may becomputed after each de-mapping step, while a second set of LLRs of thetransmitted bits of the at least one data stream may be computed aftereach decoding step.

At 730, the computed first set of LLRs may be saved for the nextre-transmission of data, if the de-mapping has been performed a definednumber of times. At 740, the computed second set of LLRs may be savedfor the next data re-transmission, if the decoding has been performedthe defined number of times. At 750, re-transmitted at least one datastream may be received. At 760, de-mapping and decoding of the receivedre-transmitted at least one data stream may be performed in theiterative manner using the stored first and second set of LLRs.

FIG. 8 illustrates another example of iterative receiver with HARQcombining in accordance with certain aspects of the present disclosure.The iterative receiver structure 800 may be utilized when consideringHARQ with the purpose of saving a memory for storing LLRs from one datatransmission to another. It can be observed from FIG. 8 that those LLRs822 stored in the post-TD buffer 832 at the output of the Turbo decoder820 may be used for both APP combining and for HARQ combining, asillustrated in FIG. 8 by adders 836 and 838, respectively. Therefore,the pre-TD buffer may not be required for HARQ combining In order tofurther save the memory, the post-TD buffer 832 may be utilized to saveonly systematic LLRs instead of all output LLRs 822 from the Turbodecoder 820.

Soft Decision Directed Channel Estimation for Iterative Receiver

Certain aspects of the present disclosure support a soft decisiondirected channel estimation for the iterative receiver 400 from FIG. 4.FIG. 9 illustrates an example iterative receiver 900 with soft decisiondirected channel estimation in accordance with certain aspects of thepresent disclosure. The receiver structure 900 may comprise theiterative receiver structure 400 from FIG. 4 and a channel estimationblock 902.

For each data transmission, initial channel estimation may be obtainedby processing a pilot signal transmitted over a pilot channel. Inaddition, at each iteration between the demapper 406 and the Turbodecoder 420 of the receiver 400, rate-matched extrinsic LLRs 408 fromthe output of Turbo decoder 420 may be utilized to refine initialchannel estimates. As illustrated in FIG. 9, the refined channelestimates 904 may be used in the next processing iteration by the TurboJLLR 404 along with the received samples 402.

FIG. 10 illustrates example operations 1000 for iterative decoding withsoft decision directed channel estimation in accordance with certainaspects of the present disclosure. At 1010, a pilot signal and at leastone data stream transmitted over a wireless channel may be received. At1020, initial estimates of the wireless channel may be computed usingthe received pilot signal. At 1030, de-mapping and decoding of the atleast one data stream may be performed using the computed initialestimates of the wireless channel, and a set of LLRs of transmitted bitsof the at least one data stream may be computed during this process. At1040, estimates of the wireless channel may be updated using thecomputed set of LLRs and the previously computed initial channelestimates. At 1050, de-mapping and decoding of the at least one datastream may be now performed using the updated estimates of the wirelesschannel. The process of updating the channel estimates may be repeatedafter de-mapping and decoding a defined number of times.

The soft decision directed channel estimation 902 is described below ingreater detail. A MIMO-OFDM wireless system may be considered with Mtransmit antennas and N receive antennas. The total number of frequencytones (i.e., subcarriers) is denoted by K. The received signal at thearbitrary k^(th) tone y_(k) may be expressed as:

y _(k) =H _(k) ·x _(k) +n _(k) , k=1,2, . . . ,K,  (6)

where y_(k)εC^(N×1), H_(k)εC^(N×M) is a MIMO channel matrix associatedwith the k^(th) tone, x_(k)εC^(M×1) represents transmitted symbols fromall M transmit antennas at the k^(th) tone, and n_(k)εC^(N×1) is a noisevector at the k^(th) tone. Let h_(k,i)εC^(1×M), iε1, 2, . . . , Ndenotes the i^(th) row of the MIMO channel matrix H_(k). Then, equation(6) may be re-written as:

$\begin{matrix}{{y_{k} = {{X_{k} \cdot h_{k}} + n_{k}}},{where}} & (7) \\{{X_{k} = \begin{bmatrix}x_{k}^{T} & 0 & \cdots & 0 \\0 & x_{k}^{T} & \cdots & 0 \\\vdots & \vdots & \ddots & \vdots \\0 & 0 & \cdots & x_{k}^{T}\end{bmatrix}_{N \times {M \cdot N}}},{and}} & (8) \\{h_{k} = {\begin{bmatrix}h_{k,1} & h_{k,2} & \cdots & h_{k,N}\end{bmatrix}^{T}.}} & (9)\end{matrix}$

If all the K tones are grouped together in a matrix form, then equation(7) may be written as:

$\begin{matrix}{{y = {{X \cdot h} + n}},{where}} & (10) \\{{y = \begin{bmatrix}y_{1}^{T} & y_{2}^{T} & \cdots & y_{K}^{T}\end{bmatrix}^{T}},} & (11) \\{{h = \begin{bmatrix}h_{1}^{T} & h_{2}^{T} & \cdots & h_{K}^{T}\end{bmatrix}^{T}},} & (12) \\{{n = \begin{bmatrix}n_{1}^{T} & n_{2}^{T} & \cdots & n_{K}^{T}\end{bmatrix}^{T}},{and}} & (13) \\{X = {\begin{bmatrix}X_{1} & 0 & \cdots & 0 \\0 & X_{2} & \cdots & 0 \\\vdots & \vdots & \ddots & \vdots \\0 & 0 & \cdots & X_{K}\end{bmatrix}.}} & (14)\end{matrix}$

It can be noted that among the K tones, a portion of them may betraining sequence used for channel estimation, while the rest may beused for actual data transmission. Let X_(t) denotes the trainingportion of X constructed by removing all rows of X containing data, andy_(t) denotes the pilot tones among the received signal y. Then, thefollowing may be written:

y _(t) =X _(t) ·h+n _(t).  (15)

For each transmitted sub-frame, the channel may be first estimated viathe pilot channel using the received pilot signal. After the Turbodecoding is performed, the output soft LLR information on data tones maybe utilized as extra information to refine the pilot-based channelestimates.

It can be assumed that E{h}=0. The linear minimum mean square error(LMMSE) channel estimator may be expressed as:

ĥ=A·y _(t).  (16)

By solving

E{(ĥ−h)·y _(t) ^(H)}=0,  (17)

the LMMSE channel estimates may be obtained as:

$\begin{matrix}{{\hat{h} = {\underset{A}{\underset{}{R_{h}{X_{t}^{H}\left( {{X_{t}R_{h}X_{t}^{H}} + R_{n_{t}}} \right)}^{- 1}}} \cdot y_{t}}},} & (18)\end{matrix}$

where R_(h) and R_(n) _(t) are cross-correlation terms related to thechannel vector h and to the noise vector n_(t). In addition, equation(18) may be written according to the matrix inversion lemma as:

ĥ=(X _(t) ^(H) R _(n) _(t) ⁻¹ X _(t) +R _(h) ⁻¹)⁻¹ X _(t) ^(H) R _(u)_(t) ⁻¹ y _(t).  (19)

Equation (18) may have the computational advantage over equation (19)since the dimension of matrix inversion in the former may be in generalsmaller than the dimension of matrix inversion in the latter. Asdescribed later in greater detail, the pilot-based channel estimationobtained as in equation (18) may be set as the initial mean value in thefollowing iterations, if an affine soft decision directed channelestimator is employed.

It can be noted from both equation (18) and equation (19) that:

E{ĥ}=0.  (20)

Thus, the covariance of the LMMSE channel estimator defined by equation(18) may be derived as:

Cov(ĥ)=E{ĥĥ ^(H) }=AX _(t) R _(h) ^(H).  (21)

Finally, the following may hold:

E{(ĥ−h) (ĥ−h)^(H) }=AX _(t) R _(h) ^(H) +R _(h)−2Re{AX _(t) R_(h)}.  (22)

In the following iterations, the covariance matrix given by equation(21) may be set as the initial covariance matrix in the case when theaffine soft decision directed channel estimator is utilized.

Certain aspects of the present disclosure support a linear soft decisiondirected channel estimation. This channel estimator may be written inthe form:

ĥ=B·y.  (23)

It can be still assumed that E{h}=0 for the iterative channel estimationprocess. The extrinsic LLRs obtained from the Turbo decoder may beutilized to construct soft symbols. The expectation s _(k,m) and avariance σ_(k,m) ² of a soft symbol corresponding to the arbitraryk^(th) (k=1, . . . , K) tone and the arbitrary m^(th) (m=1, . . . , M)transmit antenna may be readily obtained, while Gaussian approximationmay be also used.

Let

X= X+{tilde over (X)},  (24)

where X represents the expectation of X, and

E{{tilde over (X)}^(H){tilde over (X)}}={tilde over (R)}  (25)

is a diagonal matrix. For pilot tones, the diagonal terms of matrix{tilde over (R)} may be equal to zero, and for data tones the diagonalterms may be equal to σ_(k,m) ². Therefore, the following may hold:

$\begin{matrix}\begin{matrix}{y = {{Xh} + n}} \\{= {{\overset{\_}{X}h} + {\left( {\underset{\underset{z}{}}{\overset{\sim}{X}h} + n} \right).}}}\end{matrix} & (26)\end{matrix}$

It can be assumed that:

z˜CN(0,R_(z)),  (27)

where R_(z) is a diagonal matrix. Then, the corresponding diagonal termsfor pilot tones may be equal to zero, while the diagonal terms may beequal to σ_(z) ² for data tones. Under this assumption, the followingmay be obtained:

$\begin{matrix}\begin{matrix}{\sigma_{z}^{2} = {\frac{1}{K}E\left\{ {z^{H}z} \right\}}} \\{= {\frac{1}{K}E\left\{ {{tr}\left\lbrack {\overset{\sim}{X}{hh}^{H}{\overset{\sim}{X}}^{H}} \right\rbrack} \right\}}} \\{= {\frac{1}{K}{{{tr}\left\lbrack {R_{h}\overset{\sim}{R}} \right\rbrack}.}}}\end{matrix} & (28)\end{matrix}$

Thus, the linear soft decision directed channel estimator may be derivedas:

$\begin{matrix}{\hat{h} \approx {\underset{\underset{B}{}}{R_{h}{{\overset{\_}{X}}^{H}\left( {{\overset{\_}{X}R_{h}{\overset{\_}{X}}^{H}} + R_{n} + R_{z}} \right)}^{- 1}} \cdot {y.}}} & (29)\end{matrix}$

It should be noted that for the OFDM system, elements of h (i.e.,channel taps) may be strongly correlated implying that the approximationof R_(z) being diagonal may not be accurate. A simplified version of theestimator given by equation (29) may just ignore the term R_(z).

Certain aspects of the present disclosure support the affine softdecision directed channel estimation. The affine channel estimator mayhave the form of:

ĥ=Cy+ h,  (30)

where a mean of the affine channel estimator may be set as the mean ofthe pilot-based LMMSE channel estimator given by equation (18). Thesystem model may be written as:

$\begin{matrix}{\begin{matrix}{y = {{Xh} + n}} \\{= {{\left( {\overset{\_}{X} + \overset{\sim}{X}} \right) \cdot \left( {\overset{\_}{h} + \overset{\sim}{h}} \right)} + n}} \\{= {{\overset{\_}{X} \cdot \overset{\_}{h}} + {\overset{\_}{X} \cdot \overset{\sim}{h}} + {\overset{\sim}{X} \cdot \overset{\_}{h}} + {\overset{\sim}{X} \cdot \overset{\sim}{h}} + n}} \\{{= {{\overset{\_}{X} \cdot \overset{\_}{h}} + {\overset{\_}{X} \cdot \overset{\sim}{h}} + {\overset{\_}{H} \cdot \overset{\sim}{x}} + \underset{\underset{\overset{\sim}{z}}{}}{\overset{\sim}{X} \cdot \overset{\sim}{h}} + n}},}\end{matrix}{where}} & (31) \\{{H = \begin{bmatrix}{\overset{\_}{H}}_{1} & 0 & \cdots & 0 \\0 & {\overset{\_}{H}}_{2} & \cdots & 0 \\\vdots & \vdots & \ddots & \vdots \\0 & 0 & \cdots & {\overset{\_}{H}}_{K}\end{bmatrix}},{and}} & (32) \\{\overset{\sim}{x} = {\begin{bmatrix}{\overset{\sim}{x}}_{1}^{T} & {\overset{\sim}{x}}_{2}^{T} & \cdots & {\overset{\sim}{x}}_{K}^{T}\end{bmatrix}^{T}.}} & (33)\end{matrix}$

The affine soft decision directed channel estimator may be expressed as:

$\begin{matrix}{{\hat{h} \approx {{\underset{\underset{C}{}}{R_{\overset{\sim}{h}}{{\overset{\_}{X}}^{H}\left( {{\overset{\_}{X}R_{\overset{\sim}{h}}{\overset{\_}{X}}^{H}} + {\overset{\_}{H}R_{\overset{\sim}{x}}{\overset{\_}{H}}^{H}} + R_{\overset{\sim}{z}} + R_{n}} \right)}^{- 1}} \cdot \left( {y - {\overset{\_}{X} \cdot \overset{\_}{h}}} \right)} + \overset{\_}{h}}},} & (34)\end{matrix}$

where R_(z) is defined similar to R_(z) with:

$\begin{matrix}{\sigma_{\overset{\sim}{z}}^{2} = {\frac{1}{K}{{{tr}\left\lbrack {R_{\overset{\sim}{h}}\overset{\sim}{R}} \right\rbrack}.}}} & (35)\end{matrix}$

It can be noted that the assumption that R_(z) is a diagonal matrix isreasonable since elements of {tilde over (h)} may not be stronglycorrelated. A simplified version of the affine estimator given byequation (34) may ignore either or both of the termsR_({tilde over (z)}) and HR_({tilde over (x)}) H ^(H) from equation(34). Also, it can be shown that:

E{ĥ}= h,  (36)

and

Cov(ĥ)=C· X·K _(h) ^(H).  (37)

In equation (34), an initial value of R_({tilde over (h)}) may be set tothe value given by equation (22), which is obtained from the pilot-basedchannel estimation. In the following iterations, theR_({tilde over (h)}) term may be approximated by using equation (37).

The various operations of methods described above may be performed byany suitable means capable of performing the corresponding functions.The means may include various hardware and/or software component(s)and/or module(s), including, but not limited to a circuit, anapplication specific integrate circuit (ASIC), or processor. Generally,where there are operations illustrated in Figures, those operations mayhave corresponding counterpart means-plus-function components withsimilar numbering. For example, blocks 710-760 and 1010-1050 illustratedin FIGS. 7 and 10 correspond to circuit blocks 710A-760A and 1010A-1050Aillustrated in FIGS. 7A and 10A.

As used herein, the term “determining” encompasses a wide variety ofactions. For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining and the like.Also, “determining” may include receiving (e.g., receiving information),accessing (e.g., accessing data in a memory) and the like. Also,“determining” may include resolving, selecting, choosing, establishingand the like.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: a, b, or c” is intended to cover: a, b, c,a-b, a-c, b-c, and a-b-c.

The various operations of methods described above may be performed byany suitable means capable of performing the operations, such as varioushardware and/or software component(s), circuits, and/or module(s).Generally, any operations illustrated in the Figures may be performed bycorresponding functional means capable of performing the operations.

The various illustrative logical blocks, modules and circuits describedin connection with the present disclosure may be implemented orperformed with a general purpose processor, a digital signal processor(DSP), an application specific integrated circuit (ASIC), a fieldprogrammable gate array signal (FPGA) or other programmable logic device(PLD), discrete gate or transistor logic, discrete hardware componentsor any combination thereof designed to perform the functions describedherein. A general purpose processor may be a microprocessor, but in thealternative, the processor may be any commercially available processor,controller, microcontroller or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

The steps of a method or algorithm described in connection with thepresent disclosure may be embodied directly in hardware, in a softwaremodule executed by a processor, or in a combination of the two. Asoftware module may reside in any form of storage medium that is knownin the art. Some examples of storage media that may be used includerandom access memory (RAM), read only memory (ROM), flash memory, EPROMmemory, EEPROM memory, registers, a hard disk, a removable disk, aCD-ROM and so forth. A software module may comprise a singleinstruction, or many instructions, and may be distributed over severaldifferent code segments, among different programs, and across multiplestorage media. A storage medium may be coupled to a processor such thatthe processor can read information from, and write information to, thestorage medium. In the alternative, the storage medium may be integralto the processor.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

The functions described may be implemented in hardware, software,firmware or any combination thereof. If implemented in software, thefunctions may be stored as one or more instructions on acomputer-readable medium. A storage media may be any available mediathat can be accessed by a computer. By way of example, and notlimitation, such computer-readable media can comprise RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium that can be used to carryor store desired program code in the form of instructions or datastructures and that can be accessed by a computer. Disk and disc, asused herein, include compact disc (CD), laser disc, optical disc,digital versatile disc (DVD), floppy disk, and Blu-ray® disc where disksusually reproduce data magnetically, while discs reproduce dataoptically with lasers.

Thus, certain aspects may comprise a computer program product forperforming the operations presented herein. For example, such a computerprogram product may comprise a computer readable medium havinginstructions stored (and/or encoded) thereon, the instructions beingexecutable by one or more processors to perform the operations describedherein. For certain aspects, the computer program product may includepackaging material.

Software or instructions may also be transmitted over a transmissionmedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition oftransmission medium.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein can bedownloaded and/or otherwise obtained by a user terminal and/or basestation as applicable. For example, such a device can be coupled to aserver to facilitate the transfer of means for performing the methodsdescribed herein. Alternatively, various methods described herein can beprovided via storage means (e.g., RAM, ROM, a physical storage mediumsuch as a compact disc (CD) or floppy disk, etc.), such that a userterminal and/or base station can obtain the various methods uponcoupling or providing the storage means to the device. Moreover, anyother suitable technique for providing the methods and techniquesdescribed herein to a device can be utilized.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes and variations may be made in the arrangement, operation anddetails of the methods and apparatus described above without departingfrom the scope of the claims.

The techniques provided herein may be utilized in a variety ofapplications. For certain aspects, the techniques presented herein maybe incorporated in an access point station, an access terminal, or othertype of wireless device with processing logic and elements to performthe techniques provided herein.

1. A method for wireless communications, comprising: receiving at leastone data stream; de-mapping and decoding the received data stream in aniterative manner to compute a posteriori log-likelihood ratios (LLRs) ofbits of the received data stream; storing the computed a posterioriLLRs, if the decoding is performed in the iterative manner a definednumber of times; receiving re-transmitted the data stream; andde-mapping and decoding the received re-transmitted data stream in theiterative manner using the stored set of LLRs.
 2. The method of claim 1,wherein de-mapping and decoding the received data stream comprises:computing, as a result of the de-mapping the received data stream,extrinsic LLRs of the bits of the received data stream; and de-ratematching and storing the computed extrinsic LLRs, if the de-mapping ofthe received data stream is performed in the iterative manner thedefined number of times.
 3. The method of claim 2, wherein de-mappingand decoding the received re-transmitted data stream comprises:computing, as a result of the de-mapping the received re-transmitteddata stream, a first set of extrinsic LLRs of bits of the re-transmitteddata stream; de-rate matching the computed first set of extrinsic LLRs;and combining the de-rate matched first set of extrinsic LLRs and thestored extrinsic LLRs to generate input LLRs for the decoding of there-transmitted data stream.
 4. The method of claim 3, wherein de-mappingand decoding the received re-transmitted data stream further comprises:decoding the received re-transmitted data stream using the generatedinput LLRs to obtain a second set of extrinsic LLRs of the bits of there-transmitted data stream; combining the second set of extrinsic LLRsand the stored a posteriori LLRs to obtain combined LLRs, wherein the aposteriori LLRs are computed as a result of the decoding of the receiveddata stream; obtaining a priori probability (APP) LLRs of the bits ofthe re-transmitted data stream by rate matching the combined LLRs; andusing the APP LLRs for the de-mapping of the re-transmitted data stream.5. The method of claim 1, further comprising: rate matching the stored aposteriori LLRs, wherein the a posteriori LLRs are computed as a resultof the decoding of the received data stream; and using, during a firstiteration between the de-mapping and decoding of the receivedre-transmitted data stream, the rate matched LLRs as a prioriprobability (APP) LLRs of bits of the re-transmitted data stream for thede-mapping of the re-transmitted data stream.
 6. The method of claim 1,wherein de-mapping and decoding the received re-transmitted data streamcomprises: computing, as a result of the de-mapping the receivedre-transmitted data stream, a first set of extrinsic LLRs of bits of there-transmitted data stream; combining de-rate matched version of thefirst set of extrinsic LLRs and the stored a posteriori LLRs to generateinput LLRs for the decoding of the re-transmitted data stream, whereinthe a posteriori LLRs are computed as a result of the decoding of thereceived data stream; decoding the received re-transmitted data streamusing the generated input LLRs to obtain a second set of extrinsic LLRsof the bits of the re-transmitted data stream; combining the second setof extrinsic LLRs and the stored a posteriori LLRs to obtain combinedLLRs; computing a priori probability (APP) LLRs of the bits of there-transmitted data stream by rate matching the combined LLRs; and usingthe computed APP LLRs for the de-mapping of the received re-transmitteddata stream.
 7. The method of claim 6, wherein the stored a posterioriLLRs comprise only systematic LLRs associated with a systematic portionof the received data stream.
 8. An apparatus for wirelesscommunications, comprising: a receiver configured to receive at leastone data stream; a de-mapper and a decoder configured to de-map anddecode the received data stream in an iterative manner to compute aposteriori log-likelihood ratios (LLRs) of bits of the received datastream; and a buffer configured to store the computed a posteriori LLRs,if the decoding is performed in the iterative manner a defined number oftimes, wherein the receiver is also configured to receive re-transmitteddata stream, and wherein the de-mapper and the decoder are alsoconfigured to de-map and decode the received re-transmitted data streamin the iterative manner using the stored set of LLRs.
 9. The apparatusof claim 8, wherein the de-mapper and the decoder configured to de-mapand decode the received data stream comprises: a computer configured tocompute, as a result of the de-mapping the received data stream,extrinsic LLRs of the bits of the received data stream; and a circuitconfigured to de-rate match and store the computed extrinsic LLRs, ifthe de-mapping of the received data stream is performed in the iterativemanner the defined number of times.
 10. The apparatus of claim 9,wherein the de-mapper and the decoder configured to de-map and decodethe received re-transmitted data stream comprises: a processorconfigured to compute, as a result of the de-mapping the receivedre-transmitted data stream, a first set of extrinsic LLRs of bits of there-transmitted data stream; a de-rate matching circuit configured tode-rate match the computed first set of extrinsic LLRs; and a combinerconfigured to combine the de-rate matched first set of extrinsic LLRsand the stored extrinsic LLRs to generate input LLRs for the decoding ofthe re-transmitted data stream.
 11. The apparatus of claim 10, whereinthe de-mapper and the decoder configured to de-map and decode thereceived re-transmitted data stream further comprises: a decoderconfigured to decode the received re-transmitted data stream using thegenerated input LLRs to obtain a second set of extrinsic LLRs of thebits of the re-transmitted data stream; another combiner configured tocombine the second set of extrinsic LLRs and the stored a posterioriLLRs to obtain combined LLRs, wherein the a posteriori LLRs are computedas a result of the decoding of the received data stream; and a ratematching circuit configured to obtain a priori probability (APP) LLRs ofthe bits of the re-transmitted data stream by rate matching the combinedLLRs, wherein the demapper is also configured to use the APP LLRs forthe de-mapping of the re-transmitted data stream.
 12. The apparatus ofclaim 8, further comprising: a rate matching circuit configured to ratematch the stored a posteriori LLRs, wherein the a posteriori LLRs arecomputed as a result of the decoding of the received data stream,wherein the de-mapper is also configured to use, during a firstiteration between the de-mapping and decoding of the receivedre-transmitted data stream, the rate matched LLRs as a prioriprobability (APP) LLRs of bits of the re-transmitted data stream for thede-mapping of the re-transmitted data stream.
 13. The apparatus of claim8, wherein the de-mapper and the decoder configured to de-map and decodethe received re-transmitted data stream comprises: a computer configuredto compute, as a result of the de-mapping the received re-transmitteddata stream, a first set of extrinsic LLRs of bits of the re-transmitteddata stream; a combiner configured to combine de-rate matched version ofthe first set of extrinsic LLRs and the stored a posteriori LLRs togenerate input LLRs for the decoding of the re-transmitted data stream,wherein the a posteriori LLRs are computed as a result of the decodingof the received data stream; a decoder configured to decode the receivedre-transmitted data stream using the generated input LLRs to obtain asecond set of extrinsic LLRs of the bits of the re-transmitted datastream; another combiner configured to combine the second set ofextrinsic LLRs and the stored a posteriori LLRs to obtain combined LLRs;and a rate matching circuit configured to compute a priori probability(APP) LLRs of the bits of the re-transmitted data stream by ratematching the combined LLRs, wherein the de-mapper is also configured touse the computed APP LLRs for the de-mapping of the receivedre-transmitted data stream.
 14. The apparatus of claim 13, wherein thestored a posteriori LLRs comprise only systematic LLRs associated with asystematic portion of the received data stream.
 15. An apparatus forwireless communications, comprising: means for receiving at least onedata stream; means for de-mapping and decoding the received data streamin an iterative manner to compute a posteriori log-likelihood ratios(LLRs) of bits of the received data stream; means for storing thecomputed a posteriori LLRs, if the decoding is performed in theiterative manner a defined number of times; means for receivingre-transmitted the data stream; and means for de-mapping and decodingthe received re-transmitted data stream in the iterative manner usingthe stored set of LLRs.
 16. The apparatus of claim 15, wherein the meansfor de-mapping and decoding the received data stream comprises: meansfor computing, as a result of the de-mapping the received data stream,extrinsic LLRs of the bits of the received data stream; and means forde-rate matching and storing the computed extrinsic LLRs, if thede-mapping of the received data stream is performed in the iterativemanner the defined number of times.
 17. The apparatus of claim 16,wherein the means for de-mapping and decoding the receivedre-transmitted data stream comprises: means for computing, as a resultof the de-mapping the received re-transmitted data stream, a first setof extrinsic LLRs of bits of the re-transmitted data stream; means forde-rate matching the computed first set of extrinsic LLRs; and means forcombining the de-rate matched first set of extrinsic LLRs and the storedextrinsic LLRs to generate input LLRs for the decoding of there-transmitted data stream.
 18. The apparatus of claim 17, wherein themeans for de-mapping and decoding the received re-transmitted datastream further comprises: means for decoding the received re-transmitteddata stream using the generated input LLRs to obtain a second set ofextrinsic LLRs of the bits of the re-transmitted data stream; means forcombining the second set of extrinsic LLRs and the stored a posterioriLLRs to obtain combined LLRs, wherein the a posteriori LLRs are computedas a result of the decoding of the received data stream; means forobtaining a priori probability (APP) LLRs of the bits of there-transmitted data stream by rate matching the combined LLRs; and meansfor using the APP LLRs for the de-mapping of the re-transmitted datastream.
 19. The apparatus of claim 15, further comprising: means forrate matching the stored a posteriori LLRs, wherein the a posterioriLLRs are computed as a result of the decoding of the received datastream; and means for using, during a first iteration between thede-mapping and decoding of the received re-transmitted data stream, therate matched LLRs as a priori probability (APP) LLRs of bits of there-transmitted data stream for the de-mapping of the re-transmitted datastream.
 20. The apparatus of claim 15, wherein the means for de-mappingand decoding the received re-transmitted data stream comprises: meansfor computing, as a result of the de-mapping the received re-transmitteddata stream, a first set of extrinsic LLRs of bits of the re-transmitteddata stream; means for combining de-rate matched version of the firstset of extrinsic LLRs and the stored a posteriori LLRs to generate inputLLRs for the decoding of the re-transmitted data stream, wherein the aposteriori LLRs are computed as a result of the decoding of the receiveddata stream; means for decoding the received re-transmitted data streamusing the generated input LLRs to obtain a second set of extrinsic LLRsof the bits of the re-transmitted data stream; means for combining thesecond set of extrinsic LLRs and the stored a posteriori LLRs to obtaincombined LLRs; means for computing a priori probability (APP) LLRs ofthe bits of the re-transmitted data stream by rate matching the combinedLLRs; and means for using the computed APP LLRs for the de-mapping ofthe received re-transmitted data stream.
 21. The apparatus of claim 20,wherein the stored a posteriori LLRs comprise only systematic LLRsassociated with a systematic portion of the received data stream.
 22. Acomputer-program product for wireless communications, comprising acomputer-readable medium comprising instructions executable to: receiveat least one data stream; de-map and decode the received data stream inan iterative manner to compute a posteriori log-likelihood ratios (LLRs)of bits of the received data stream; store the computed a posterioriLLRs, if the decoding is performed in the iterative manner a definednumber of times; receive re-transmitted data stream; and de-map anddecode the received re-transmitted data stream in the iterative mannerusing the stored set of LLRs.
 23. A wireless node, comprising: at leastone antenna; a receiver configured to receive at least one data streamvia the at least one antenna; a de-mapper and a decoder configured tode-map and decode the received data stream in an iterative manner tocompute a posteriori log-likelihood ratios (LLRs) of bits of thereceived data stream; and a buffer configured to store the computed aposteriori LLRs, if the decoding is performed in the iterative manner adefined number of times, wherein the receiver is also configured toreceive re-transmitted data stream via the at least one antenna, andwherein the de-mapper and the decoder are also configured to de-map anddecode the received re-transmitted data stream in the iterative mannerusing the stored set of LLRs.
 24. A method for wireless communications,comprising: receiving a pilot signal and at least one data streamtransmitted over a wireless channel; computing initial estimates of thewireless channel using the received pilot signal; de-mapping, de-ratematching and decoding the received data stream using the computedinitial estimates of the wireless channel to compute a set oflog-likelihood ratios (LLRs) of transmitted bits of the data stream;updating estimates of the wireless channel using the computed set ofLLRs and the computed initial estimates of the wireless channel; andde-mapping, de-rate matching and decoding the received data stream usingthe updated estimates of the wireless channel.
 25. The method of claim24, wherein the computed set of LLRs comprises extrinsic LLRs generatedas a result of the decoding.
 26. The method of claim 25, furthercomprising: rate-matching the extrinsic LLRs to obtain the computed setof LLRs.
 27. The method of claim 24, wherein updating the estimates ofthe wireless channel comprises: obtaining hard symbols associated withthe received data stream using the computed set of LLRs; obtaining softsymbols associated with the received data stream using the computed setof LLRs; and computing the estimates of the wireless channel using theobtained hard symbols and statistics of the obtained soft symbols. 28.The method of claim 24, wherein the estimates of the wireless channelare updated according to an affine soft decision directed channelestimation algorithm.
 29. The method of claim 24, wherein the estimatesof the wireless channel are updated according to a linear soft decisiondirected channel estimation algorithm.
 30. An apparatus for wirelesscommunications, comprising: a receiver configured to receive a pilotsignal and at least one data stream transmitted over a wireless channel;an estimator configured to compute initial estimates of the wirelesschannel using the received pilot signal; and a de-mapper, a de-ratematching circuit and a decoder configured to de-map, de-rate match anddecode the received data stream using the computed initial estimates ofthe wireless channel to compute a set of log-likelihood ratios (LLRs) oftransmitted bits of the data stream, wherein the estimator is alsoconfigured to update estimates of the wireless channel using thecomputed set of LLRs and the computed initial estimates of the wirelesschannel, and wherein the de-mapper, the de-rate matching circuit and thedecoder are also configured to de-map, de-rate match and decode thereceived data stream using the updated estimates of the wirelesschannel.
 31. The apparatus of claim 30, wherein the computed set of LLRscomprises extrinsic LLRs generated as a result of the decoding.
 32. Theapparatus of claim 31, further comprising: a rate matching circuitconfigured to rate-match the extrinsic LLRs to obtain the computed setof LLRs.
 33. The apparatus of claim 30, wherein the estimator configuredto update the estimates of the wireless channel comprises: a processorconfigured to obtain hard symbols associated with the received datastream using the computed set of LLRs; another processor configured toobtain soft symbols associated with the received data stream using thecomputed set of LLRs; and a computer configured to compute the estimatesof the wireless channel using the obtained hard symbols and statisticsof the obtained soft symbols.
 34. The apparatus of claim 30, wherein theestimates of the wireless channel are updated according to an affinesoft decision directed channel estimation algorithm.
 35. The apparatusof claim 30, wherein the estimates of the wireless channel are updatedaccording to a linear soft decision directed channel estimationalgorithm.
 36. An apparatus for wireless communications, comprising:means for receiving a pilot signal and at least one data streamtransmitted over a wireless channel; means for computing initialestimates of the wireless channel using the received pilot signal; meansfor de-mapping, de-rate matching and decoding the received data streamusing the computed initial estimates of the wireless channel to computea set of log-likelihood ratios (LLRs) of transmitted bits of the datastream; means for updating estimates of the wireless channel using thecomputed set of LLRs and the computed initial estimates of the wirelesschannel; and means for de-mapping, de-rate matching and decoding thereceived data stream using the updated estimates of the wirelesschannel.
 37. The apparatus of claim 36, wherein the computed set of LLRscomprises extrinsic LLRs generated as a result of the decoding.
 38. Theapparatus of claim 37, further comprising: means for rate-matching theextrinsic LLRs to obtain the computed set of LLRs.
 39. The apparatus ofclaim 36, wherein the means for updating the estimates of the wirelesschannel comprises: means for obtaining hard symbols associated with thereceived data stream using the computed set of LLRs; means for obtainingsoft symbols associated with the received data stream using the computedset of LLRs; and means for computing the estimates of the wirelesschannel using the obtained hard symbols and statistics of the obtainedsoft symbols.
 40. The apparatus of claim 36, wherein the estimates ofthe wireless channel are updated according to an affine soft decisiondirected channel estimation algorithm.
 41. The apparatus of claim 36,wherein the estimates of the wireless channel are updated according to alinear soft decision directed channel estimation algorithm.
 42. Acomputer-program product for wireless communications, comprising acomputer-readable medium comprising instructions executable to: receivea pilot signal and at least one data stream transmitted over a wirelesschannel; compute initial estimates of the wireless channel using thereceived pilot signal; de-map, de-rate match and decode the receiveddata stream using the computed initial estimates of the wireless channelto compute a set of log-likelihood ratios (LLRs) of transmitted bits ofthe data stream; update estimates of the wireless channel using thecomputed set of LLRs and the computed initial estimates of the wirelesschannel; and de-map, de-rate match and decode the received data streamusing the updated estimates of the wireless channel.
 43. A wirelessnode, comprising: at least one antenna; a receiver configured to receivevia the at least one antenna a pilot signal and at least one data streamtransmitted over a wireless channel; an estimator configured to computeinitial estimates of the wireless channel using the received pilotsignal; and a de-mapper, a de-rate matching circuit and a decoderconfigured to de-map, de-rate match and decode the received data streamusing the computed initial estimates of the wireless channel to computea set of log-likelihood ratios (LLRs) of transmitted bits of the datastream, wherein the estimator is also configured to update estimates ofthe wireless channel using the computed set of LLRs and the computedinitial estimates of the wireless channel, and wherein the de-mapper,the de-rate matching circuit and the decoder are also configured tode-map, de-rate match and decode the received data stream using theupdated estimates of the wireless channel.